Buscar
Mostrando ítems 11-20 de 23
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data
(BMC, 2022)
Background: Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Performance of Algorithms for Interval Discretization of Biomedical Signals
(Springer, 2016)
A methodology to quantify the dependence be tween features using the Ameva discretization algorithm and the advantages of qualitative models is presented in this paper. This approach will be applied over medical data ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Towards a unified model representation of machine learning knowledge
(SciTePress, 2019)
Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Robustness Testing of a Machine Learning-based Road Object Detection System: An Industrial Case
(IEEE Computer Society, 2022)
artifi-cial intelligence (AI), methods have been proposed and evaluated in academia to assess the reliability of these systems. In the context of computer vision, some approaches use the generation of images altered by ...
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Improving models for environmental applications of LiDAR: Novel approaches based on soft computing
(IOS Press, 2016)
This work proposes novel methodologies to improve the use of Light Detection And Ranging (LiDAR) for environ mental purposes, especially for thematic mapping (LiDAR only or fused with other remote sensors) and the estimation ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Un Recorrido por los Principales Proveedores de Servicios de Machine Learning y Predicción en la Nube
(Sociedad de Ingeniería de Software y Tecnologías de Desarrollo de Software (SISTEDES), 2018)
Los medios tecnológicos para el consumo, producción e intercambio de información no hacen más que aumentar cada día que pasa. Nos encontramos envueltos en el fenómeno Big Data, donde ser capaces de analizar esta informa ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Indexes to Find the Optimal Number of Clusters in a Hierarchical Clustering
(Springer, 2019)
Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mining. Most clustering methods require establishing the number of clusters beforehand. However, due to the size of the data ...
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Automated Deployment of a Spark Cluster with Machine Learning Algorithm Integration
(Elsevier, 2020)
The vast amount of data stored nowadays has turned big data analytics into a very trendy research field. The Spark distributed computing platform has emerged as a dominant and widely used paradigm for cluster deployment ...
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
LEAPME: learning-based property matching with embeddings
(Elsevier, 2022)
Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their ...
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
CAFE: Knowledge graph completion using neighborhood-aware features
(Elsevier, 2021)
Knowledge Graphs (KGs) currently contain a vast amount of structured information in the form of entities and relations. Because KGs are often constructed automatically by means of information extraction processes, they ...